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Jürg P. Keller

Jürg P. Keller
Prof. Dr.
Jürg P. Keller
Senior Scientist
FHNW
juerg.keller1@fhnw.ch
Curriculum Vitae
Incorporate 'intelligence' into dynamic system is a highly motivating task. NCCR offers a great chance to develop new solutions in data based control.

Jürg Keller is Professor for Control, Image Processing and Machine Learning at the University of Applied Science Northwestern Switzerland. He obtained his PhD degree in Control Systems  from the ETH, Zürich in 1989. From 1989 to 1990 he had the opportunity to do a postdoc at the Australian National University with B.D.O. Anderson. From 1990 to 1995 he was automation engineer at Hoffmann-La Roche, Basel, where he developped modern concepts for automation of flexible pharmaceutical plants. After 1995 he build up the institute of automation. His research is focused on data-based plant control and optimization.   He was president of the IFAC national member organization, SGA from 2005 - 2019.

Research projects as Researcher

Title
Principal Investigators

Framework for industrial control of CPS

Alisa Rupenyan, John Lygeros

Summary

This project is focused on the development of a data-driven framework for self-maintenance and self-optimization for autonomous (robot-assisted) manufacturing. The framework will be characterized by three main components:

  1. Sensing and data processing
  2. Detection of faults and warnings using the extracted features 
  3. Process control and optimization (maintaining safety, optimizing performance

The acquired process data will be used to optimise the manufacturing process by optimising trajectories of the assisting robots, and process parameters, to improve manufacturing productivity.

People involved

Jürg P. Keller
Alisa Rupenyan, John Lygeros, Jürg P. Keller
Framework for industrial control of CPS
Alisa Rupenyan, John Lygeros

This project is focused on the development of a data-driven framework for self-maintenance and self-optimization for autonomous (robot-assisted) manufacturing. The framework will be characterized by three main components:

  1. Sensing and data processing
  2. Detection of faults and warnings using the extracted features 
  3. Process control and optimization (maintaining safety, optimizing performance

The acquired process data will be used to optimise the manufacturing process by optimising trajectories of the assisting robots, and process parameters, to improve manufacturing productivity.

Jürg P. Keller
Alisa Rupenyan, John Lygeros, Jürg P. Keller
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74f86789-d97c-4673-a478-7907c8fc7c98